Vehicle loads have significant impacts on the emissions of heavy-duty trucks, even in the same traffic conditions. Few studies exist covering the differences in emissions of diesel semi-trailer towing trucks (DSTTTs) with different loads, although these vehicles have a wide load range. In this context, the operating modes and emission rates of DSTTTs were analyzed under varying loads scenarios to understand the effect of vehicle loads on emission factors. First, second-by-second field speed data and emission data of DSTTTs with different loads were collected. Then, the methods for calculating the scaled tractive power (STP) and the emissions model for DSTTTs were proposed to evaluate the effect of different loading scenarios. The STP distributions, emission rate distributions, and emission factor characteristics of different loaded trucks were analyzed and compared. The results indicated that the STP distributions of DSTTTs that under the unloaded state were more narrow than those under fully loaded or overloaded conditions. The emission rates of carbon dioxide (CO₂), carbon monoxide (CO) and total hydrocarbon (THC) for DSTTTs under a fully loaded state were significantly higher than those under an unloaded state. However, due to the influence of exhaust temperature, the emission rates of nitrogen oxides (NO_x) among fully loaded trucks were lower than those under the unloaded state when STP bin was above 4 kW/ton. The emission factors of CO₂, CO, THC, and NO_x for fully loaded trucks demonstrated the largest increases at low-speed intervals (0–30 km/h), which rose by 96.2%, 47.9%, 27.8%, and 65.2%, respectively.

With the rapid increase of automobile ownership, the city of Beijing, China, faces challenging issues with regard to traffic congestion and vehicle emissions. In seeking solutions to these issues, developing and improving public transportation systems is considered one of the most feasible and promising strategies because these systems have high passenger-carrying capacity and low pollution on a per passenger basis. Driving cycles reflect traffic conditions, and thus vehicle emissions, and form a scientific base to further improve bus operations. This paper is intended to develop city-specific driving cycles for transit buses for Beijing. The operational data of 126 buses were collected by using a portable global positioning system (GPS) for three types of bus routes: the bus rapid transit (BRT) line, express line, and regular line. Through analysis of the operating characteristics, a methodology for developing driving cycles is proposed based on the parameter of vehicle-specific power (VSP) distr...

The development of new fuel consumption and emission models creates the need to characterize traffic conditions by using vehicle-specific power (VSP) distribution. However, in existing transportation engineering, there has been a lack of knowledge of relationships between the VSP distribution and commonly used traffic parameters and a lack of models to develop the VSP distribution from traffic parameters. To examine how traffic conditions affect VSP distributions, this study uses large samples of floating car data collected from expressways in Beijing to associate VSP distributions with various average travel speeds. After a comprehensive analysis, regular patterns are found between the VSP distribution and the average travel speed. Specifically, when the average travel speed is more than 20 km/h, the VSP distribution comes close to a normal distribution. The mean of the VSP distribution is the VSP value when cruising at the average travel speed, and the standard deviation could be expressed as a power f...

Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

This paper presents a microscopic traffic simulation approach to the capacity impact analysis of weaving sections caused by the installation of exclusive bus lanes (XBLs) on urban expressways. Three typical configurations of XBL in Beijing are identified, including median bus lane with off-on-ramp, curbside bus lane with on-off-ramp, and curbside bus lane with off-on-ramp. Then, the weaving section length, headway, mainline volume, as well as off-ramp and on-ramp volumes for general traffic are considered as parameters in designing simulation scenarios. Finally, the microscopic simulation is executed, by which the capacity of general traffic for weaving sections is estimated and the impact of the weaving section length and headway on the capacity is evaluated. It is found that the weaving section length and headway have different impacts on the capacity of general traffic in different configurations. The results also provide that the weaving section length and headway are more sensitive in the on-off-ramp than in the off-on-ramp scenario with the curb bus lane.

The primary objective of this research is to develop a practical model for evaluating the effects of traffic management on fuel efficiency. The relationships between the real-world driving activities, the vehicle specific power, and the corresponding fuel consumptions are analyzed based on the data of 26 gasoline and liquefied petroleum gas-fueled light-duty vehicles (LDVs). Then, an indicator for evaluating the level of fuel consumption is designed by dividing the normalized fuel consumption by the fuel use in the most economic scenario. An aggregate model is developed following the design of the indicator that features minimal input requirement and meaningful output for LDVs. In the case studies, the proposed model is applied to estimating and comparing the fuel efficiency of several driving cycles, evaluating the fuel efficiency improvement resulting from the electronic toll collection system, and developing temporal variations of fuel efficiency for a road in Beijing by using floating car data. Finall...